根据其他列的值填充熊猫的缺失值

Question

I have a following dataframe:

    A    B   C   D
0  NaN  2.0 NaN  0
1  3.0  4.0 NaN  1
2  NaN  NaN NaN  5
3  NaN  3.0 NaN  4

Now I want to fill null values of A with the values in B or D. ie if the value is Null in B than check D. So resultant dataframe looks like this.

   A    B   C    D
0  2.0  2.0 NaN  0
1  3.0  4.0 NaN  1
2  5    NaN NaN  5
3  3.0  3.0 NaN  4

I can do this using following code:

df['A'] = df['A'].fillna(df['B'])
df['A'] = df['A'].fillna(df['D'])

But I want to do this in one line, how can I do that?

Answer 1

You could simply chain both .fillna() :

df['A'] = df.A.fillna(df.B).fillna(df.D)

    A    B   C   D
0  2.0  2.0 NaN  0
1  3.0  4.0 NaN  1
2  5.0  NaN NaN  5
3  3.0  3.0 NaN  4

---

Or using fillna with combine_first :

df['A'] = df.A.fillna(df.B.combine_first(df.D))

Answer 2

If dont need chain because many columns better is use back filling missing values with seelcting first column by positions:

df['A'] = df['A'].fillna(df[['B','D']].bfill(axis=1).iloc[:, 0])
print (df)
     A    B   C  D
0  2.0  2.0 NaN  0
1  3.0  4.0 NaN  1
2  5.0  NaN NaN  5
3  3.0  3.0 NaN  4